South Central University for Nationalities, College of Biomedical Engineering, Wuhan, 430074, People's Republic of China.
Huibei Key Laboratory for Medical Information Analysis and Tumor Treatment, Wuhan, 430074, People's Republic of China.
J Digit Imaging. 2018 Feb;31(1):107-116. doi: 10.1007/s10278-017-0017-z.
In this paper, we present a multi-texton representation method for medical image retrieval, which utilizes the locality constraint to encode each filter bank response within its local-coordinate system consisting of the k nearest neighbors in texton dictionary and subsequently employs spatial pyramid matching technique to implement feature vector representation. Comparison with the traditional nearest neighbor assignment followed by texton histogram statistics method, our strategies reduce the quantization errors in mapping process and add information about the spatial layout of texton distributions and, thus, increase the descriptive power of the image representation. We investigate the effects of different parameters on system performance in order to choose the appropriate ones for our datasets and carry out experiments on the IRMA-2009 medical collection and the mammographic patch dataset. The extensive experimental results demonstrate that the proposed method has superior performance.
本文提出了一种用于医学图像检索的多纹理表示方法，该方法利用局部约束在由纹理词典中 k 个最近邻组成的局部坐标系内对每个滤波器组响应进行编码，然后采用空间金字塔匹配技术实现特征向量表示。与传统的最近邻分配后进行纹理直方图统计方法相比，我们的策略减少了映射过程中的量化误差，并增加了纹理分布的空间布局信息，从而提高了图像表示的描述能力。我们研究了不同参数对系统性能的影响，以便为我们的数据集选择合适的参数，并在 IRMA-2009 医学数据集和乳腺图像补丁数据集上进行了实验。广泛的实验结果表明，所提出的方法具有优越的性能。
